Analysis of CoCoSO-CRITIC method for optimizing diesel engine performance with biodiesel-nano-additive blends

It is critical to quickly investigate alternatives to petroleum-based fuels because of the high demand and continuing need for fossil fuels. This research takes this challenge into account by experimentally examining the effect of different levels of critical input parameters on the long-term performance characteristics of a engine that runs on a new mixture of bio-diesel and diesel fuel with the addition of cerium oxide. We take four different levels of ignition pressure (IP in bar), load engine (LD %), blended percentage (B %), and concentration of nanoparticles (NPC -ppm) into account as inputs. For diesel engines, Taguchi’s L16 standard orthogonal array is used to assess emission measurements and performance metrics. To find the optimum values for the input parameters that produce the greatest results across all performance metrics, a hybrid multi-criteria decision-making (MCDM) technique combines the CoCoSO-CRITIC method with the signal-to-noise ratio (SNR) and analysis of means (ANOM). Forty percent, eighty parts per million, fifty percent, and two hundred bar are the ideal values for B, NPC, LD, and optimal output responses, respectively, when all input parameters are taken into account at the same time. Blend (5.43%), engine load (11.48%), ignition pressure (5.45%), and nanoparticle concentration (63.06%) are the most significant variables, according to the analysis of variance (ANOVA) findings. After a validation test, the following optimization outcomes were confirmed: Among the parameters measured were BTE at 34.18%, BSEC at 0.296, CO at 0.019%, NOx at 560.3 ppm, UBHC at 29.2 ppm, and VL at 20.06 m2/sec. For both present and future energy needs, these hybrid fuels provide a green alternative.